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Found 10 papers, 1 papers with code
Micro Energy-Water-Hydrogen Nexus: Data-driven Real-time Optimal Operation
This paper extends a new concept of energy-water-hydrogen (EWH) nexus, which was recently developed as a solution for reducing carbon emissions from the generation side of power systems, to the distribution side.
Economic Viability of the Energy-Water-Hydrogen Nexus for Power System Decarbonization
This paper aims to evaluate the economic viability of the energy-water-hydrogen (EWH) nexus as a new solution for reducing carbon emissions from power systems.
End-to-End Streaming Video Temporal Action Segmentation with Reinforce Learning
The end-to-end SVTAS which regard TAS as an action segment clustering task can expand the application scenarios of TAS; and RL is used to alleviate the problem of inconsistent optimization objective and direction.
Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures: High-fidelity Active Constraints and Integer Variables Prediction Enhanced by Feature Space Expansion
It results in only a small-scale continuous convex optimization problem needing to be solved by optimization solvers for W2H-LCCI real-time optimal operation.
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection
This paper proposes to coordinate the operation of the two systems via a fully decentralized framework where the PDN and TN operators solve their own operation problems by sharing only limited information.
Human Gait Recognition Using Bag of Words Feature Representation Method
In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method.
Security-Constrained Optimal Operation of Energy-Water Nexus based on a Fast Contingency Filtering Method
The optimal power and water flow are obtained in a normal situation by considering the set of contingencies that can not be controlled with corrective actions.
Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling
A multi-period optimal PSO under uncertainty is formulated using the chance-constrained optimization (CCO) modeling paradigm, where the constraints include the nonlinear energy storage and AC power flow models.
Ensemble learning based linear power flow
This paper develops an ensemble learning-based linearization approach for power flow, which differs from the network-parameter based direct current (DC) power flow or other extended versions of linearization.
Ensemble Learning Based Convex Approximation of Three-Phase Power Flow
The proposed approach is based on quadratic power flow equations in rectangular coordinates and it can be used in both balanced and unbalanced three-phase power networks.
